


'''simple docstring'''



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    @slow
    @require_torch
    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      List[Any]														=						EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,	'prajjwal1/bert-tiny'      )
         __lowerCamelCase  :      Tuple														=						BertTokenizer.from_pretrained('bert-base-uncased'      )

         __lowerCamelCase  :      Optional[int]														=						bertabert.config.encoder.vocab_size
         __lowerCamelCase  :      Any														=						tokenizer.sep_token_id
         __lowerCamelCase  :      List[Any]														=						tokenizer.cls_token_id
         __lowerCamelCase  :      List[str]														=						1_28

         __lowerCamelCase  :      Dict														=						datasets.load_dataset('cnn_dailymail'     ,	'3.0.0'     ,	split='train[:1%]'      )
         __lowerCamelCase  :      str														=						datasets.load_dataset('cnn_dailymail'     ,	'3.0.0'     ,	split='validation[:1%]'      )

         __lowerCamelCase  :      Dict														=						train_dataset.select(range(32      )      )
         __lowerCamelCase  :      str														=						val_dataset.select(range(16      )      )

         __lowerCamelCase  :      Any														=						4

         def _map_to_encoder_decoder_inputs(SCREAMING_SNAKE_CASE_      ):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              __lowerCamelCase  :      int														=						tokenizer(batch['article']     ,	padding='max_length'     ,	truncation=SCREAMING_SNAKE_CASE_     ,	max_length=5_12      )
              __lowerCamelCase  :      List[Any]														=						tokenizer(batch['highlights']     ,	padding='max_length'     ,	truncation=SCREAMING_SNAKE_CASE_     ,	max_length=1_28      )
              __lowerCamelCase  :      str														=						inputs.input_ids
              __lowerCamelCase  :      List[str]														=						inputs.attention_mask

              __lowerCamelCase  :      Optional[Any]														=						outputs.input_ids
              __lowerCamelCase  :      Dict														=						outputs.input_ids.copy()
              __lowerCamelCase  :      Tuple														=						[
                  [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              __lowerCamelCase  :      Union[str, Any]														=						outputs.attention_mask

              assert all(len(SCREAMING_SNAKE_CASE_      ) == 5_12 for x in inputs.input_ids      )
              assert all(len(SCREAMING_SNAKE_CASE_      ) == 1_28 for x in outputs.input_ids      )

              return batch

         def _compute_metrics(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Tuple														=						pred.label_ids
              __lowerCamelCase  :      Optional[Any]														=						pred.predictions

              # all unnecessary tokens are removed
              __lowerCamelCase  :      List[Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Tuple														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

              __lowerCamelCase  :      List[Any]														=						sum([int(pred_str[i] == label_str[i]      ) for i in range(len(SCREAMING_SNAKE_CASE_      )      )]      ) / len(SCREAMING_SNAKE_CASE_      )

              return {"accuracy": accuracy}

         # map train dataset
         __lowerCamelCase  :      List[Any]														=						train_dataset.map(
             _map_to_encoder_decoder_inputs     ,	batched=SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	remove_columns=['article', 'highlights']     ,	)
         train_dataset.set_format(
             type='torch'     ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,	)

         # same for validation dataset
         __lowerCamelCase  :      List[Any]														=						val_dataset.map(
             _map_to_encoder_decoder_inputs     ,	batched=SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	remove_columns=['article', 'highlights']     ,	)
         val_dataset.set_format(
             type='torch'     ,	columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,	)

         __lowerCamelCase  :      int														=						self.get_auto_remove_tmp_dir()

         __lowerCamelCase  :      Dict														=						SeqaSeqTrainingArguments(
             output_dir=SCREAMING_SNAKE_CASE_     ,	per_device_train_batch_size=SCREAMING_SNAKE_CASE_     ,	per_device_eval_batch_size=SCREAMING_SNAKE_CASE_     ,	predict_with_generate=SCREAMING_SNAKE_CASE_     ,	evaluation_strategy='steps'     ,	do_train=SCREAMING_SNAKE_CASE_     ,	do_eval=SCREAMING_SNAKE_CASE_     ,	warmup_steps=0     ,	eval_steps=2     ,	logging_steps=2     ,	)

         # instantiate trainer
         __lowerCamelCase  :      Any														=						SeqaSeqTrainer(
             model=SCREAMING_SNAKE_CASE_     ,	args=SCREAMING_SNAKE_CASE_     ,	compute_metrics=_compute_metrics     ,	train_dataset=SCREAMING_SNAKE_CASE_     ,	eval_dataset=SCREAMING_SNAKE_CASE_     ,	tokenizer=SCREAMING_SNAKE_CASE_     ,	)

         # start training
         trainer.train()








"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


